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All Journal Bulletin of Electrical Engineering and Informatics Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) International Journal of Applied Sciences and Smart Technologies
Siti Yuliyanti
Siliwangi University
Agriculture, Biological Sciences & Forestry Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Public Health Social Sciences

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Improving the Performance of Posyandu Cadres with SIPPOS: Posyandu Service Information System in the Sangkali Tamansari Community Health Center Area Tasikmalaya City: Peningkatan Kinerja Kader Posyandu dengan SIPPOS: Sistem Informasi Pelayanan Posyandu di Wilayah Puskesmas Sangkali Tamansari Kota Tasikmalaya Heni Sulastri; Siti Yuliyanti; Neng Ika Kurniati; Muhammad Al-Husaini; Hen Hen Lukmana
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 10 No. 1 (2026): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v10i1.480

Abstract

Posyandu cadres have a strategic role in the promotion and prevention of public health services at the primary level. However, many cadres rely on manual recording systems that are less efficient. This community service focuses on training, mentoring, and monitoring the implementation of the system with the aim of improving the performance of Posyandu cadres through the implementation of SIPPOS (Posyandu Service Information System). SIPPOS is an information system to assist Posyandu cadres in managing health service data more quickly. Community service activities include SIPPOS socialization to cadres and health center officers, including training on system usage and technical assistance. Evaluation of the effectiveness of SIPPOS use in routine Posyandu activities, and program sustainability in order to expand the benefits of the community service reach. This community service is expected to significantly improve Posyandu cadres in mastery of information technology and the accuracy of service data recording.
Principal Component Analysis-Driven Feature Reduction for Predicting Coffee Quality Using a Machine Learning Approach Siti Yuliyanti; Heni Sulastri; Sakifah
International Journal of Applied Sciences and Smart Technologies Vol. 8 No. 1 (2026): Volume 08, Issue 1, June 2026
Publisher : Faculty of Science and Technology, Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/pmjkr989

Abstract

Coffee quality assessment using a machine learning approach faces major challenges, including high data dimensionality and redundancy between features. Therefore, PCA is proposed as a feature reduction technique to improve the efficiency and accuracy of coffee quality prediction models. The research phase began with data acquisition, data cleaning, feature engineering, explanatory data analysis, testing the normalization of coffee parameter profiles, implementing PCA on Random Forest and XGBoost models, and then evaluating model performance. Model evaluation using MAE and MAPE showed that Random Forest provided more precise predictions than XGBoost, particularly when applying PCA. This resulted in a 39% performance increase for Random Forest from 0.11903 to 0.08542 and an 8% increase for XGBoost, shifting the score from 0.12511 to 0.11570. Prediction visualization reinforced the consistency and precision of the Random Forest model, regardless of whether PCA was used. The findings of this study highlight the importance of feature cleaning and engineering, and the role of PCA in improving the precision of coffee quality predictions. The use of the Random Forest model with PCA is recommended as an efficient method for modeling the quality of Arabica coffee, taking into account sensory and environmental factors.
Flood mapping using Res-Q and machine learning on imbalanced data Siti Yuliyanti; Vega Purwayoga; Andi Nur Rachman; Zakwan Gusnadi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10374

Abstract

Flood disaster mapping requires accurate methods to support early warning and mitigation planning. To address common issues such as imbalanced data distribution and limited attribute handling, this study proposes an improved approach. The methodology includes: i) modification of the spatial sort filter skyline method with reverse normalization based on attribute preferences, applied when an attribute has minimal preference to ensure balanced consideration during skyline filtering; ii) data labeling and balancing, where initial flood potential labeling is generated using Res-Q, followed by K-Means clustering to group data into four classes (low, moderate, high, and very high) and SMOTE to further balance the dataset with 558 data points per class; iii) model evaluation using the C5.0 algorithm under three schemes, showing high and consistent accuracy with 89.24% on imbalanced data (Schema 2) and 93.3% on balanced data (Schema 3), while Schema 1 shows overfitting due to extreme imbalance; and iv) the main contribution, integrating reverse normalization with skyline filtering combined with clustering and resampling, enhancing both accuracy and robustness in identifying flood-prone areas. This structured approach highlights methodological improvements, reliable results, and practical contributions for effective flood disaster management.
Co-Authors Andi Nur Rachman Hen Hen Lukmana Heni Sulastri Muhammad Al-Husaini Neng Ika Kurniati Sakifah Vega Purwayoga Zakwan Gusnadi